Evidence map›Paper›PMID 29747692›Full record

ArticleMicrobiome2018

Low-abundant bacteria drive compositional changes in the gut microbiota after dietary alteration.

Jacquelynn Benjamino, Stephen Lincoln, Ranjan Srivastava, Joerg Graf

Abstract read
In one paragraph

Article in Microbiome, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

59 citing papers in PubMed.

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  8. Gut Microbiota Response in Meagre (Animals : an open access journal from MDPI · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jacquelynn BenjaminoDepartment of Molecular and Cell Biology, University of Connecticut, 91 N. Eagleville Road, U-3125, Storrs, CT, 06269, USA.
Stephen LincolnDepartment of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT, USA.
Ranjan SrivastavaDepartment of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT, USA.
Joerg GrafDepartment of Molecular and Cell Biology, University of Connecticut, 91 N. Eagleville Road, U-3125, Storrs, CT, 06269, USA. Joerg.graf@uconn.edu.

Funding

National Science Foundation 1137249
6 · The paper itself

Abstract

backgroundAs the importance of beneficial bacteria is better recognized, understanding the dynamics of symbioses becomes increasingly crucial. In many gut symbioses, it is essential to understand whether changes in host diet play a role in the persistence of the bacterial gut community. In this study, termites were fed six dietary sources and the microbial community was monitored over a 49-day period using 16S rRNA gene sequencing. A deep backpropagation artificial neural network (ANN) was used to learn how the six different lignocellulose food sources affected the temporal composition of the hindgut microbiota of the termite as well as taxon-taxon and taxon-substrate interactions.

resultsShifts in the termite gut microbiota after diet change in each colony were observed using 16S rRNA gene sequencing and beta diversity analyses. The artificial neural network accurately predicted the relative abundances of taxa at random points in the temporal study and showed that low-abundant taxa maintain community driving correlations in the hindgut.

conclusionsThis combinatorial approach utilizing 16S rRNA gene sequencing and deep learning revealed that low-abundant bacteria that often do not belong to the core community are drivers of the termite hindgut bacterial community composition.

Indexed as

AnimalsBacteriaBase SequenceDietDNA, BacterialGastrointestinal MicrobiomeGastrointestinal TractIsopteraLigninSequence Analysis, DNASymbiosisDNA, BacterialLigninlignocellulose16S rRNA gene sequencingArtificial neural networkDeep learningLow-abundant driversTermite microbiota

Identifiers

PMID29747692
PMCPMC5944116

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.